{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:I2XYOR6CWQBSHLCTKWBMFXQDWX","short_pith_number":"pith:I2XYOR6C","canonical_record":{"source":{"id":"2605.17071","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T16:42:43Z","cross_cats_sorted":[],"title_canon_sha256":"2a9270ca2728d4e422ee1da173440ed5ad11747954a664401c37c908d1893448","abstract_canon_sha256":"203a7eb92927f2676199260b858832860b70350411d330c5682cf26696729c4c"},"schema_version":"1.0"},"canonical_sha256":"46af8747c2b40323ac535582c2de03b5d3991c582057a006e44b34adc3e2c18c","source":{"kind":"arxiv","id":"2605.17071","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17071","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17071v1","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17071","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"pith_short_12","alias_value":"I2XYOR6CWQBS","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"pith_short_16","alias_value":"I2XYOR6CWQBSHLCT","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"pith_short_8","alias_value":"I2XYOR6C","created_at":"2026-05-20T00:03:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:I2XYOR6CWQBSHLCTKWBMFXQDWX","target":"record","payload":{"canonical_record":{"source":{"id":"2605.17071","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T16:42:43Z","cross_cats_sorted":[],"title_canon_sha256":"2a9270ca2728d4e422ee1da173440ed5ad11747954a664401c37c908d1893448","abstract_canon_sha256":"203a7eb92927f2676199260b858832860b70350411d330c5682cf26696729c4c"},"schema_version":"1.0"},"canonical_sha256":"46af8747c2b40323ac535582c2de03b5d3991c582057a006e44b34adc3e2c18c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:39.170679Z","signature_b64":"FpmtnKj3B2+QA2g+Y065p6jNX0DAgGEGXS+5bYX8YvB4OrXwsvkgzO6iHyoSlBo5hD1Lh0qtTqa/iPNx4Ci9DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46af8747c2b40323ac535582c2de03b5d3991c582057a006e44b34adc3e2c18c","last_reissued_at":"2026-05-20T00:03:39.170112Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:39.170112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.17071","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qTFuV97RM8WylMRD/GBP6+a/Duxx08ry4wGfC0BtcPTi79QnilTcDHl6FPXnnsKw+cOccSEDscgzpFAjcjfVDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:32:08.087981Z"},"content_sha256":"d1172806ab343f05fbd274a40f0e3279cd72e58575571dee915bea07e2106998","schema_version":"1.0","event_id":"sha256:d1172806ab343f05fbd274a40f0e3279cd72e58575571dee915bea07e2106998"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:I2XYOR6CWQBSHLCTKWBMFXQDWX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AnchorDiff: Topology-Aware Masked Diffusion with Confidence-based Rewriting for Radiology Report Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Guoming Lu, Jielei Wang, Shiying Yu","submitted_at":"2026-05-16T16:42:43Z","abstract_excerpt":"Radiology report generation (RRG) aims to automatically produce clinically accurate textual reports from medical images. Existing methods predominantly rely on autoregressive (AR) language models, whose causal dependency structure restricts generation to a unidirectional left-to-right process. This paradigm can induce sequence bias, where models tend to follow stereotypical token orders and high-frequency report templates rather than fully grounding generation in image-specific evidence. In this paper, we propose AnchorDiff, the first masked-diffusion framework for RRG that integrates knowledg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17071","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.17071/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T22:33:23.813958Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T22:21:57.752268Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"8584fd6fe0b3267da57121fb7ce7bc20eb911464f1ac75a172a7e82d63d081ef"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5aviJiiZurBOZlIIVKMaF+B/MuNjh9/q1zy7uek6O8uiSlEP1BBZzPu0zT79tSTyC67eUyvqvQ/xQ1HdYx2JCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:32:08.088448Z"},"content_sha256":"326055034b34012e7404d6cb3ef759c6a9cf4ee4ad46ddf2dda8b9614d3c5e67","schema_version":"1.0","event_id":"sha256:326055034b34012e7404d6cb3ef759c6a9cf4ee4ad46ddf2dda8b9614d3c5e67"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I2XYOR6CWQBSHLCTKWBMFXQDWX/bundle.json","state_url":"https://pith.science/pith/I2XYOR6CWQBSHLCTKWBMFXQDWX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I2XYOR6CWQBSHLCTKWBMFXQDWX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-21T14:32:08Z","links":{"resolver":"https://pith.science/pith/I2XYOR6CWQBSHLCTKWBMFXQDWX","bundle":"https://pith.science/pith/I2XYOR6CWQBSHLCTKWBMFXQDWX/bundle.json","state":"https://pith.science/pith/I2XYOR6CWQBSHLCTKWBMFXQDWX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I2XYOR6CWQBSHLCTKWBMFXQDWX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:I2XYOR6CWQBSHLCTKWBMFXQDWX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"203a7eb92927f2676199260b858832860b70350411d330c5682cf26696729c4c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T16:42:43Z","title_canon_sha256":"2a9270ca2728d4e422ee1da173440ed5ad11747954a664401c37c908d1893448"},"schema_version":"1.0","source":{"id":"2605.17071","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17071","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17071v1","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17071","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"pith_short_12","alias_value":"I2XYOR6CWQBS","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"pith_short_16","alias_value":"I2XYOR6CWQBSHLCT","created_at":"2026-05-20T00:03:39Z"},{"alias_kind":"pith_short_8","alias_value":"I2XYOR6C","created_at":"2026-05-20T00:03:39Z"}],"graph_snapshots":[{"event_id":"sha256:326055034b34012e7404d6cb3ef759c6a9cf4ee4ad46ddf2dda8b9614d3c5e67","target":"graph","created_at":"2026-05-20T00:03:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T22:33:23.813958Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T22:21:57.752268Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.17071/integrity.json","findings":[],"snapshot_sha256":"8584fd6fe0b3267da57121fb7ce7bc20eb911464f1ac75a172a7e82d63d081ef","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Radiology report generation (RRG) aims to automatically produce clinically accurate textual reports from medical images. Existing methods predominantly rely on autoregressive (AR) language models, whose causal dependency structure restricts generation to a unidirectional left-to-right process. This paradigm can induce sequence bias, where models tend to follow stereotypical token orders and high-frequency report templates rather than fully grounding generation in image-specific evidence. In this paper, we propose AnchorDiff, the first masked-diffusion framework for RRG that integrates knowledg","authors_text":"Guoming Lu, Jielei Wang, Shiying Yu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T16:42:43Z","title":"AnchorDiff: Topology-Aware Masked Diffusion with Confidence-based Rewriting for Radiology Report Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17071","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d1172806ab343f05fbd274a40f0e3279cd72e58575571dee915bea07e2106998","target":"record","created_at":"2026-05-20T00:03:39Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"203a7eb92927f2676199260b858832860b70350411d330c5682cf26696729c4c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T16:42:43Z","title_canon_sha256":"2a9270ca2728d4e422ee1da173440ed5ad11747954a664401c37c908d1893448"},"schema_version":"1.0","source":{"id":"2605.17071","kind":"arxiv","version":1}},"canonical_sha256":"46af8747c2b40323ac535582c2de03b5d3991c582057a006e44b34adc3e2c18c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"46af8747c2b40323ac535582c2de03b5d3991c582057a006e44b34adc3e2c18c","first_computed_at":"2026-05-20T00:03:39.170112Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:39.170112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FpmtnKj3B2+QA2g+Y065p6jNX0DAgGEGXS+5bYX8YvB4OrXwsvkgzO6iHyoSlBo5hD1Lh0qtTqa/iPNx4Ci9DA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:39.170679Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17071","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d1172806ab343f05fbd274a40f0e3279cd72e58575571dee915bea07e2106998","sha256:326055034b34012e7404d6cb3ef759c6a9cf4ee4ad46ddf2dda8b9614d3c5e67"],"state_sha256":"ca76bbd2b64e64743e3033f4a544a7ae2afcc3b78685563375870ab5ea27ad8d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UzZioV6A/QXGaGmn7lmr9jbH9NgAuOaxPEvJC+rElFWbkMncg/cwGvsD/ZWfn5eMB9UN9+7RPJC7gu3yAFYNCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T14:32:08.090632Z","bundle_sha256":"e6c4a767794e9f360c77093e311e8f7162400d3e584751226088b6a68fb5d0b5"}}